@inproceedings{68399554bd554083af7d6afdf56e6d32,
title = "An Exploration of Large Language Models for Verification of News Headlines",
abstract = "This study explores the capabilities of ChatGPT in news headline verification across different prompts and languages. We introduce an optimal prompt design and a novel difficulty ratio metric to analyze ChatGPT's performance across various statement resources. Our findings highlight ChatGPT's promising accuracy and cross-linguistic adaptability in fact-checking, while also identifying areas for further investigation, especially in expanding the analysis beyond headlines and exploring other Large Language Models (LLMs). Our findings suggest it is highly promising to leverage an LLM such as ChatGPT as a general tool in combating misinformation, enhancing the trustworthiness of digital information, and increasing trust-worithiness of text data mining algorithms by providing more reliable data sources for the algorithms.",
keywords = "Information trust, large language models, misinformation, prompting",
author = "Yifan Li and Zhai, {Cheng Xiang}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023 ; Conference date: 01-12-2023 Through 04-12-2023",
year = "2023",
doi = "10.1109/ICDMW60847.2023.00032",
language = "English (US)",
series = "IEEE International Conference on Data Mining Workshops, ICDMW",
publisher = "IEEE Computer Society",
pages = "197--206",
editor = "Jihe Wang and Yi He and Dinh, {Thang N.} and Christan Grant and Meikang Qiu and Witold Pedrycz",
booktitle = "Proceedings - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023",
}